-
Book Overview & Buying
-
Table Of Contents
The Complete Self-Driving Car Course - Applied Deep Learning
By :
The Complete Self-Driving Car Course - Applied Deep Learning
By:
Overview of this book
Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world. This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You’ll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company.
This course will show you how to do the following:
- Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
- Train a perceptron-based neural network to classify between binary classes
- Train convolutional neural networks to identify various traffic signs
- Train deep neural networks to fit complex datasets
- Master Keras, a power neural network library written in Python
- Build and train a fully functional self-driving car
All the code and supporting files for this course are available at https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning
Table of Contents (14 chapters)
Introduction
Python Crash Course
NumPy Crash Course
Computer Vision: Finding Lane Lines
The Perceptron
Deep Neural Networks
Multiclass Classification
MNIST Image Recognition
Convolutional Neural Networks
Classifying Road Symbols
Polynomial Regression
This video is locked
